class: title-slide, bottom, right background-image: url(https://images.unsplash.com/photo-1570386230314-9fe8204c6f80?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1108&q=80) background-size: cover ### Plotting # ### **Miriam Lerma**<br> June 2023 --- name: index class: title-slide, inverse # Index - [index](#index) - [theory](#theory) - [scatterplot](#scatterplot) - [line chart](#lines) - [barplot](#barplot) - [trajectory](#trajectory) - [maps](#maps) - [aesthetics](#aesthetics) - [theme](#theme) - [color](#color) - [background](#background) - [axis](#axis) - [maps](#custom-map) - [shapefiles](#shapefiles) - [limits](#limits) - [scale](#scale) - [arrow](#arrow) - [export](#export) - [contact](#out) --- class: title-slide, inverse # Today .pull-left[ **Your profile** - You know how to use a Rmd document - You know how to install packages **Goals of today** - Create plots and maps in R - Change the apparence of the plots - Export you plots **Pauses and questions** - Exercises and 10 minute pauses for catching up - You can stop me to ask questions or use [this link <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#f2cc8f;overflow:visible;position:relative;"><path d="M432,320H400a16,16,0,0,0-16,16V448H64V128H208a16,16,0,0,0,16-16V80a16,16,0,0,0-16-16H48A48,48,0,0,0,0,112V464a48,48,0,0,0,48,48H400a48,48,0,0,0,48-48V336A16,16,0,0,0,432,320ZM488,0h-128c-21.37,0-32.05,25.91-17,41l35.73,35.73L135,320.37a24,24,0,0,0,0,34L157.67,377a24,24,0,0,0,34,0L435.28,133.32,471,169c15,15,41,4.5,41-17V24A24,24,0,0,0,488,0Z"/></svg>]( https://docs.google.com/document/d/1uG7a2_hkdaKQm5gKXRBFf6gcyoUBan2e69gL3ZKcwg8/edit?usp=sharing) ] .pull-right[ We will use the packages ```r devtools::install_github("MiriamLL/sula") devtools::install_github("MiriamLL/GermanNorthSea") devtools::install_github("MiriamLL/seamonas") install.packages('sf') install.packages('ggspatial') install.packages('plotly') ``` ] --- class: inverse # References .pull-left[ Tutorials/Books - [gg is for Grammar of Graphics](https://pkg.garrickadenbuie.com/trug-ggplot2)<br> - [Introduccion to ggplot](https://englelab.gatech.edu/useRguide/introduction-to-ggplot2.html) - [r4ds](https://r4ds.had.co.nz/data-visualisation.html) - [Geocomputation with R](https://r.geocompx.org/attr.html) - [R Graphics Cookbook](https://r-graphics.org/) - [Allison Horst tutorial](https://allisonhorst.github.io/rice-data-viz/) Gallery/Portafolios - [Data-Viz-Bookmarks](https://spcanelon.notion.site/spcanelon/Data-Viz-Bookmarks-dc01718020bd4fd6a8a4ca80e6bce933)<br> - [RGraphGallery](https://www.r-graph-gallery.com/) <br> - [DataToViz](https://www.data-to-viz.com/) <br> - [Coolors](https://coolors.co/) <br> ] .pull-right[ <img src="https://cdn.myportfolio.com/45214904-6a61-4e23-98d6-b140f8654a40/9a306c0a-dac8-413d-ba2e-cc7fd4c4d5c8_rw_1920.png?h=c802991088a9623f1f7aa18c470797ee" height="300" /> ] --- name: theory ## 1.1. Theory The objective of a graph/plot/map/chart is to communicate. <br> In science, the idea is to show information in a clear and understandable way. <img src="http://www.typesofgraphs.com/wp-content/uploads/2015/12/Typesofgraphs.png" height="350" /> Fuente: [VizThinker](https://paldhous.github.io/ucb/2016/dataviz/week2.html) --- ## 1.1. Theory Plotting is transforming data to visualizations that vary in shape, size and colors. <img src="https://paldhous.github.io/ucb/2016/dataviz/img/class2_1.jpg" height="350" /> Fuente: [VizThinker](https://paldhous.github.io/ucb/2016/dataviz/week2.html) --- ## 1.1. Theory There are many ways to plot data, and different ways to interpretate the graphs. <img src="https://paldhous.github.io/ucb/2016/dataviz/img/class2_2.jpg" height="350" /> Fuente: [VizThinker](https://paldhous.github.io/ucb/2016/dataviz/week2.html) --- ## 1.1. Theory Which one is more intuitive? <img src="https://paldhous.github.io/ucb/2016/dataviz/img/class2_4.jpg" height="350" /> Fuente: [VizThinker](https://paldhous.github.io/ucb/2016/dataviz/week2.html) --- ## 1.1. Theory Which one should I use? You can try different plots and choose the one that betters tells the story. **Tableau Public** has a suggestion on which graph to use according to the type of data you are using. <img src="https://paldhous.github.io/ucb/2016/dataviz/img/class2_5.jpg" height="300" /> --- name: intro ## 1.2. Intro There are many packages to create plots in R. - Here we will use **ggplot2**. ggplot is the most versatile one, and from which you can find more help and inspiration. --- ## 1.2. Intro A graph can be divided in three main components: - **Data** (data): which are the variables that we plot. - **Geometry** (geom): a geometric object such as dots, lines, bars. - **Aesthetics** (aes): the aesthetic attributes to a geometric object, such as position, color, shape and size. Each component is displayed as **layers** like in GIS (Geographic information systems). <img src="https://englelab.gatech.edu/useRguide/images/dataviz/ge_geom.png" height="200" /> Fuente: [ggplot2 book](https://englelab.gatech.edu/useRguide/working-with-data-overview.html) --- ## 1.3. Data The package ggplot is already included in tidyverse. <br> If you have installed tidyverse you just need to call your package. ```r library(tidyverse) ``` If not you can also install it separately. ```r install.packages("ggplot2") ``` --- ## 1.3. Data For the exercises, we can use data from packages.<br> Lets add our object to our **environment**. Package with data ```r library(palmerpenguins) ``` ``` ## Warning: package 'palmerpenguins' was built under R version 4.2.3 ``` ```r penguins<-penguins ``` Call library **ggplot2**. ```r library(ggplot2) ``` --- ## 1.3. Data First step: add data to a plot. ```r ggplot(data=penguins) ``` <!-- --> We told ggplot what data frame we want to use, but need to give instruction on how to plot. <br> --- ## 1.3. Add axis Using **mapping** we tell ggplot what to plot in the x and y axis.<br> **Note** that the axis are columns from your data frame. This is when having tidydata is important. ```r ggplot(data=penguins, * mapping=aes(x=bill_length_mm, * y=bill_depth_mm)) ``` <!-- --> --- ## 1.3. Geometry The next step is to give it a geometry, in this case we say plot points with the argument **geom_point** ```r ggplot(data=penguins ,mapping=aes(x=bill_length_mm,y=bill_depth_mm))+ * geom_point() ``` <!-- --> It would likely show a **warning**. <br> This is because NAs are not going to be plotted and R is trying to warn you. --- ## 1.3. Geometry There are many **geometries**. Most, if not all, of them start with **geom_*** <img src="https://github.com/MiriamLL/R_intro/blob/master/Images/302geometries.png?raw=true" height="400" /> --- ## 1.3. Geometry Examples of most common geometries are to be found on [R-graph-gallery](https://r-graph-gallery.com/) .pull-left[ <img src="https://github.com/MiriamLL/R_intro/blob/master/Images/302examples.png?raw=true" height="150" /> <img src="https://github.com/MiriamLL/R_intro/blob/master/Images/306examples.png?raw=true" height="200" /> ] .pull-right[ <img src="https://github.com/MiriamLL/R_intro/blob/master/Images/307examples.png?raw=true" height="200" /> ] --- name: scatterplot ## 1.4. Scatterplots The scatterplot is most useful for displaying the relationship between two continuous variables. Each dot represents an observation. Their position on the x (horizontal) and y (vertical) axis represents the values of the 2 variables. The argument to create scatterplots is **geom_point** ```r ggplot(data=penguins, mapping=aes(x=bill_length_mm,y=bill_depth_mm))+ * geom_point() ``` <!-- --> See more [scatterplots](https://r-graph-gallery.com/scatterplot.html) --- name: lines ## 1.4. Line chart A line chart or line graph displays the evolution of one or several numeric variables. <br> Data points are connected by straight line segments. The argument to create line charts is **geom_line()** ```r ggplot(data=penguins, mapping=aes(x=bill_length_mm,y=bill_depth_mm))+ * geom_line() ``` <!-- --> See more [line chart](https://r-graph-gallery.com/line-plot.html) --- name: barplot ## 1.4. Barplot A barplot is used to display the relationship between a numeric and a categorical variable.<br> The argument to create barplots is **geom_bar()** ```r ggplot(data=penguins, mapping=aes(x=bill_length_mm,y=bill_depth_mm))+ * geom_bar() ``` Error: stat_count() can only have an x or y aesthetic. Different from the logic of the previous plots, we need to tell what do you want inside the bars by adding **stats=**. --- ## 1.4. Barplot The argument **stats=** is to be included inside the parethesis of **geom_bar** ```r ggplot(data=penguins, mapping=aes(x=bill_length_mm,y=bill_depth_mm))+ * geom_bar(stat='identity') ``` <!-- --> See more [barplots](https://r-graph-gallery.com/barplot.html) --- name: boxplot ## 1.4. Boxplot Boxplot is probably the most commonly used chart type to compare distribution of several groups.<br> It visualises five summary statistics (the median, two perfcentiles and two whiskers). <img src="https://github.com/MiriamLL/R_intro/blob/master/Images/305boxplot.png?raw=true" height="350" /> Source: [Boxplot explanation](https://www.leansigmacorporation.com/box-plot-with-minitab/) --- ## 1.4. Boxplot The boxplot compactly displays the distribution of a continuous variable. The argument to create boxplots is **geom_boxplot()** ```r ggplot(data=penguins, mapping=aes(x=bill_length_mm,y=bill_depth_mm))+ * geom_boxplot() ``` <!-- --> --- ## 1.4. Boxplot Often we will like to use boxplots to visualize differences between groups. Using the argument **group=* inside mapping to create boxplots per group. ```r ggplot(data=penguins, mapping=aes(x=bill_length_mm,y=bill_depth_mm, * group=species))+ geom_boxplot() ``` <!-- --> See more [boxplots](https://r-graph-gallery.com/boxplot.html) --- name: trajectory ## 1.4. Trajectory For plotting moving objects (such as planes or birds), connecting points with a line is the most common visualization used. -- For today exercises, install **sula**. ```r devtools::install_github("MiriamLL/sula") ``` The package **sula** contains tracking data from Masked boobies on Easter Island. ```r library(sula) ``` To plot a trajectory, lets load data from the package **sula**. Then, get data in the environment. ```r tracking_data<-sula::GPS_raw ``` The data frame contains the columns **Latitude** and **Longitude**. --- ## 1.4. Trajectory The argument to create trajectories is **geom_path()**. By definition geom_path() connects the observations in the order in which they appear in the data. ```r ggplot(data=tracking_data, mapping=aes(x=Latitude, y=Longitude)) + * geom_path() ``` <!-- --> --- name: maps ## 1.4. Maps R is an great tool for geospatial data analysis. The book [geocomputation with R](https://r.geocompx.org/attr.html) is a recommended guide to create maps in R (and is constantly updated). <img src="https://github.com/MiriamLL/R_intro/blob/master/Images/306Book.png?raw=true" height="400" /> --- ## 1.4. Maps For today exercises, install **GermanNorthSea**. ```r devtools::install_github("MiriamLL/GermanNorthSea") ``` The package **GermanNorthSea** compiles shapefiles from the North Sea. ```r library(GermanNorthSea) ``` To see the containing shapefiles go to my [github](https://github.com/MiriamLL/GermanNorthSea). To add shapefile to environment. ```r Europe<-German_land ``` **Note** that shapefiles can be an **sf** object. An sf object is different from a data frame, if we click on the object is not clear what it contains. ```r class(Europe) ``` ``` ## [1] "sf" "data.frame" ``` --- ## 1.4. Maps The argument to plot **sf** (simple features) objects is **geom_sf()** By definition geom_sf() uses a unique aesthetic: geometry, giving an column of class sfc containing simple features data. ```r ggplot(data=Europe)+ * geom_sf() ``` <!-- --> --- ## 1.4. Maps To **zoom-in**, the argument **coord_sf()** can be used. **For this map, I am using the CRS 3035**. Thus, my **xlim** and my **ylim** are in those units. ```r ggplot(data=Europe)+ geom_sf()+ * coord_sf(xlim = c(3790000,4250000), ylim = c(3350000,3680000)) ``` <!-- --> --- ## 1.4. Maps Although I will not give details on CRS's today, A common CRS (Coordinate Reference System) is 4326. To transform our shapefile to other coordinate system **st_transform** from the package **sf** can be used. ```r Europe<-sf::st_transform(Europe, 4326) ``` Now the xlim and ylim can be in degrees. ```r ggplot(data=Europe)+ geom_sf()+ * coord_sf(xlim = c(3,9), ylim = c(53,56)) ``` <!-- --> --- ## 1.5. Simplify We can skip writing **data=** and **mapping=** by just following the order of the elements. .pull-left[ Including the elements. ```r ggplot(data=penguins, mapping=aes(x=bill_length_mm,y=flipper_length_mm))+ geom_point() ``` <!-- --> ] .pull-right[ Simplified example. ```r ggplot(penguins, aes(x=bill_length_mm,y=flipper_length_mm,))+ geom_point() ``` <!-- --> ] --- ## 1.6. Common mistakes The most common mistakes with plotting are the same as while writing code. <img src="https://github.com/MiriamLL/R_intro/blob/master/Images/023DebuggingBingo.jpg?raw=true" height="450" /> --- ## 1.6. Common mistakes Not having **tidydata** is also a very common problem. So remember: <img src="https://github.com/MiriamLL/R_intro/blob/master/Images/104TidyData.jpg?raw=true" height="400" /> [Allison Horst](https://allisonhorst.com/) --- ## 1.6. Common mistakes Other common mistakes specific to ggplot are: - Forgetting a plus symbol ( **+** ) ```r ggplot(data=penguins, mapping=aes(x=bill_length_mm,y=flipper_length_mm)) geom_point() ``` - Adding the **+** on the wrong order ```r ggplot(data=penguins, mapping=aes(x=bill_length_mm,y=flipper_length_mm)) +geom_point() ``` - Forgetting a parenthesis ```r ggplot(penguins, * aes(x=bill_length_mm,y=flipper_length_mm)+ geom_point() ``` --- class: inverse # Pause .pull-left[ Practice <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#FFFFFF;overflow:visible;position:relative;"><path d="M497.9 142.1l-46.1 46.1c-4.7 4.7-12.3 4.7-17 0l-111-111c-4.7-4.7-4.7-12.3 0-17l46.1-46.1c18.7-18.7 49.1-18.7 67.9 0l60.1 60.1c18.8 18.7 18.8 49.1 0 67.9zM284.2 99.8L21.6 362.4.4 483.9c-2.9 16.4 11.4 30.6 27.8 27.8l121.5-21.3 262.6-262.6c4.7-4.7 4.7-12.3 0-17l-111-111c-4.8-4.7-12.4-4.7-17.1 0zM124.1 339.9c-5.5-5.5-5.5-14.3 0-19.8l154-154c5.5-5.5 14.3-5.5 19.8 0s5.5 14.3 0 19.8l-154 154c-5.5 5.5-14.3 5.5-19.8 0zM88 424h48v36.3l-64.5 11.3-31.1-31.1L51.7 376H88v48z"/></svg><br> Create a plot from zero ] .pull-right[ Until here: - [index](#index) - [theory](#theory) - [scatterplot](#scatterplot) - [line chart](#lines) - [barplot](#barplot) - [trajectory](#trajectory) - [maps](#maps) Next part: - [aesthetics](#aesthetics) - [theme](#theme) - [color](#color) - [background](#background) - [axis](#axis) - [maps](#custom-map) - [shapefiles](#shapefiles) - [limits](#limits) - [scale](#scale) - [arrow](#arrow) - [export](#export) - [contact](#out) ] --- name: aesthetics class: title-slide, inverse, bottom background-image: url(https://images.unsplash.com/photo-1460661419201-fd4cecdf8a8b?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D&auto=format&fit=crop&w=580&q=80) background-size:cover .pull-right[ # <span style=" font-weight: bold; color: #e5e5e5 !important;border-radius: 4px; padding-right: 4px; padding-left: 4px; background-color: #003049 !important;" >Aesthetics</span> ### <span style=" font-weight: bold; color: #e5e5e5 !important;border-radius: 4px; padding-right: 4px; padding-left: 4px; background-color: #003049 !important;" >Bring some colors</span> ] --- # 2. Aesthetics **Aesthetics** are for controlling the overall appearance of graphs. You can change almost every tiny detail to create the plot that you want. <img src="https://github.com/MiriamLL/R_intro/blob/master/Images/308parts-ggplot.png?raw=true" height="350" /> Source: [ggplot2 elements](https://isabella-b.com/blog/ggplot2-theme-elements-reference/)<br> Download [pdf](https://isabella-b.com/files/ggplot2-theme-elements-reference-v2.pdf) --- # 2. Aesthetics There are many elements, but I will not go into detail to most of them. For going into more detail the book [R Graphics cookbook](https://r-graphics.org/) is a great guide. <img src="https://github.com/MiriamLL/R_intro/blob/master/Images/310Cookbook.jpg?raw=true" height="300" /> --- name: theme ## 2.1. Theme Themes are a powerful way to customize the non-data components of your plots: i.e. titles, labels, fonts, background, gridlines, and legends. Themes can be used to give plots a consistent customized look. .pull-left[ The theme can be changed using **theme_classic()** ```r ggplot(data=penguins, mapping=aes(x=bill_length_mm,y=flipper_length_mm))+ geom_point()+ * theme_classic() ``` <!-- --> ] .pull-right[ he theme can be changed using **theme_bw()** ```r ggplot(data=penguins, mapping=aes(x=bill_length_mm,y=flipper_length_mm))+ geom_point()+ * theme_bw() ``` <!-- --> ] --- ## 2.1. Theme There are many different themes. It would depend on the type of graph and taste to choose. .pull-left[ he theme can be changed using **theme_minimal()** ```r ggplot(data=penguins, mapping=aes(x=bill_length_mm,y=flipper_length_mm))+ geom_point()+ * theme_minimal() ``` <!-- --> ] .pull-right[ he theme can be changed using **theme_void()** ```r ggplot(data=penguins, mapping=aes(x=bill_length_mm,y=flipper_length_mm))+ geom_point()+ * theme_void() ``` <!-- --> ] --- name: color ## 2.2. Color To go beyond the default colors, the package ggplot2 allows you to change colors on the elements of your plot. To select the colors there are several ways, I use by **name** or **hex codes**. Using by **name**, the default names are used. .pull-left[ You can see a complete list of the 657 colors typing colors(). ```r colors() ``` ] .pull-right[ You can also see the color and the name by opening this [link](https://github.com/MiriamLL/R_intro/blob/master/Images/311colornamesplot.png?raw=true). <img src="https://github.com/MiriamLL/R_intro/blob/master/Images/311colornamesplot.png?raw=true" height="300" /> ] --- ## 2.2. Color To use the color by **name** in your plot you can add it, for example, inside the brackets of geom_point() ```r ggplot(data=penguins, mapping=aes(x=bill_length_mm,y=flipper_length_mm))+ * geom_point(color='red') ``` <!-- --> --- ## 2.2. Color Colors can also be defined by their **hex code**. An hex code is the code of the color in 6 digits. To find the hex code there are tons of webpages. I personally like [coolors](https://coolors.co/palettes/trending). In [coolors](https://coolors.co/palettes/trending) go to explore>put your cursor on top of the color you like>click>paste on your code <img src="https://github.com/MiriamLL/R_intro/blob/master/Images/312coolors.png?raw=true" height="300" /> --- ## 2.2. Color To use the color by **hex code** in your plot you can add it, for example, inside the brackets of geom_point(). **Note** that different from using colors by name, here you need to add a **#**. ```r ggplot(data=penguins, mapping=aes(x=bill_length_mm,y=flipper_length_mm))+ * geom_point(color='#c1121f') ``` <!-- --> --- ## 2.2. Color If you wish to colour point on a scatterplot by a third categorical variable, then add **colour =** within your aes brackets. This tells ggplot that this third variable will colour the points. ```r ggplot(data=penguins, mapping=aes(x=bill_length_mm,y=flipper_length_mm, * color=species))+ geom_point() ``` <!-- --> --- ## 2.2. Color The colors, when not declared, are selected by default.<br> To customize your colors you can add more arguments. <br> The argument **scale_colour_manual** allows you to customize those colors. <br> You can add colors by **name**.<br> ```r ggplot(data=penguins, mapping=aes(x=bill_length_mm,y=flipper_length_mm, color=species))+ geom_point()+ * scale_colour_manual(values = c("purple", "yellow", "orange")) ``` <!-- --> --- ## 2.2. Color For a map, we can change the color of our shapefile by adding the argument **colour** and **fill** inside the brackets of **geom_sf()** ```r ggplot(data = Europe)+ geom_sf(color = "olivedrab4", fill = "darkseagreen") ``` <!-- --> --- name: background ## 2.3. Background colors To change the background, we can add the arguments inside the argument **theme**. I added here the **coord_sf** to focus in a specific area. ```r ggplot(data = Europe)+ geom_sf(color = "olivedrab4", fill = "darkseagreen")+ * theme(panel.background = element_rect(fill = 'lightskyblue2'))+ coord_sf(xlim = c(3,9), ylim = c(53,56)) ``` <!-- --> --- ## 2.3. Background colors To remove the grids from the background, we can add **element_blank** inside the argument **theme**. ```r ggplot(data = Europe)+ geom_sf(color = "olivedrab4", fill = "darkseagreen")+ theme( * panel.grid.major = element_blank(), * panel.grid.minor = element_blank(), panel.background = element_rect(fill = 'lightskyblue2'))+ coord_sf(xlim = c(3,9), ylim = c(53,56)) ``` <!-- --> --- name: axis ## 2.4. Axis legends To change the legends on the axis, we can add the arguments **xlab** and **ylab** ```r ggplot(data = Europe)+ geom_sf(color = "olivedrab4", fill = "darkseagreen")+ theme( panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_rect(fill = 'lightskyblue2'))+ coord_sf(xlim = c(3,9), ylim = c(53,56))+ * xlab('Longitude')+ylab('Latitude') ``` <!-- --> --- ## 2.4. Axis legends To change the axis labels, it is usually more simple with other plots, but with maps we need to specify with the argument **scale_x_continuous** and **scale_y_continuous**. ```r ggplot(data = Europe)+ geom_sf(color = "olivedrab4", fill = "darkseagreen")+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_rect(fill = 'lightskyblue2'))+ coord_sf(xlim = c(3,9), ylim = c(53,56))+ * scale_x_continuous(labels = function(x) paste0(x, '\u00B0', "W")) + * scale_y_continuous(labels = function(x) paste0(x, '\u00B0', "N")) ``` <!-- --> --- ## 2.5. Object To also simplify the process, you can save your plot as an object in the environment. ```r My_plot<-ggplot(data = Europe)+ geom_sf(color = "olivedrab4", fill = "darkseagreen") ``` And add all the elements to your object using the **+** ```r *My_plot+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_rect(fill = 'lightskyblue2'))+ coord_sf(xlim = c(3,9), ylim = c(53,56))+ scale_x_continuous(labels = function(x) paste0(x, '\u00B0', "W")) + scale_y_continuous(labels = function(x) paste0(x, '\u00B0', "N")) ``` <!-- --> --- ## 2.6. Cheat sheet Help>CheatSheets>Data Visualization with ggplot <img src="https://github.com/MiriamLL/R_intro/blob/master/Images/314cheatsheet.png?raw=true" height="400" /> --- class: inverse # Pause .pull-left[ Practice <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#FFFFFF;overflow:visible;position:relative;"><path d="M497.9 142.1l-46.1 46.1c-4.7 4.7-12.3 4.7-17 0l-111-111c-4.7-4.7-4.7-12.3 0-17l46.1-46.1c18.7-18.7 49.1-18.7 67.9 0l60.1 60.1c18.8 18.7 18.8 49.1 0 67.9zM284.2 99.8L21.6 362.4.4 483.9c-2.9 16.4 11.4 30.6 27.8 27.8l121.5-21.3 262.6-262.6c4.7-4.7 4.7-12.3 0-17l-111-111c-4.8-4.7-12.4-4.7-17.1 0zM124.1 339.9c-5.5-5.5-5.5-14.3 0-19.8l154-154c5.5-5.5 14.3-5.5 19.8 0s5.5 14.3 0 19.8l-154 154c-5.5 5.5-14.3 5.5-19.8 0zM88 424h48v36.3l-64.5 11.3-31.1-31.1L51.7 376H88v48z"/></svg><br> Change the theme, color, or other elements of the following map. ```r ggplot(data = Europe)+ geom_sf(color = _______, fill = _______)+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_rect(fill = _______))+ coord_sf(xlim = c(3,9), ylim = c(53,56))+ scale_x_continuous(labels = function(x) paste0(x, '\u00B0', "W")) + scale_y_continuous(labels = function(x) paste0(x, '\u00B0', "N")) ``` ] .pull-right[ Until here: - [index](#index) - [theory](#theory) - [scatterplot](#scatterplot) - [line chart](#lines) - [barplot](#barplot) - [trajectory](#trajectory) - [maps](#maps) - [aesthetics](#aesthetics) - [theme](#theme) - [color](#color) - [background](#background) - [axis](#axis) Next part: - [maps](#custom-map) - [shapefiles](#shapefiles) - [limits](#limits) - [scale](#scale) - [arrow](#arrow) - [export](#export) - [contact](#out) ] --- name: custom-maps class: title-slide, inverse, bottom background-image: url(https://images.unsplash.com/photo-1554033551-050e208bcaf2?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D&auto=format&fit=crop&w=870&q=800) background-size: cover .pull-right[ # <span style=" font-weight: bold; color: #e5e5e5 !important;border-radius: 4px; padding-right: 4px; padding-left: 4px; background-color: #003049 !important;" >Maps</span> ### <span style=" font-weight: bold; color: #e5e5e5 !important;border-radius: 4px; padding-right: 4px; padding-left: 4px; background-color: #003049 !important;" >Level up</span> ] --- ## 3.Maps Some elements from a map - Scale - Arrow --- ## 3.1. Load shapefile Call the package sf to read the shapefiles into R ```r library(sf) ``` ``` ## Linking to GEOS 3.9.3, GDAL 3.5.2, PROJ 8.2.1; sf_use_s2() is TRUE ``` Call the package here to work in your directory. ```r library(here) ``` ``` ## here() starts at C:/Users/lerma/OneDrive/Documents/2MonTrack/2023/Teaching/R_intro/R_intro ``` Or manually add the directory where your shapefile is. ```r here() ``` ``` ## [1] "C:/Users/lerma/OneDrive/Documents/2MonTrack/2023/Teaching/R_intro/R_intro" ``` ```r My_directory<-here('Downloads') list.files(My_directory) ``` ``` ## [1] "Europe.dbf" "Europe.prj" "Europe.sbn" "Europe.sbx" ## [5] "Europe.shp" "Europe.shx" "My_plot.png" "penguins1.csv" ## [9] "penguins2.csv" "penguins3.txt" "penguins4.xlsx" ``` ```r My_shapefile<-paste0(My_directory,"/Europe.shp") ``` Download [shapefile]() You can download shapefiles from: https://www.naturalearthdata.com/downloads/ --- ## 3.1. Load shapefile ```r Europe_shp<-st_read(My_shapefile) ``` ``` ## Reading layer `Europe' from data source ## `C:\Users\lerma\OneDrive\Documents\2MonTrack\2023\Teaching\R_intro\R_intro\Downloads\Europe.shp' ## using driver `ESRI Shapefile' ## Simple feature collection with 54 features and 2 fields ## Geometry type: MULTIPOLYGON ## Dimension: XY ## Bounding box: xmin: -31.26575 ymin: 32.39748 xmax: 69.07032 ymax: 81.85737 ## Geodetic CRS: WGS 84 ``` --- ## 3.2. Create a basic map Load the package ggplot2 ```r library(ggplot2) ``` Plot your shapefile using **geom_sf** ```r ggplot(data = Europe_shp)+ geom_sf() ``` <!-- --> --- ## 3.3. Add colors I copy the hex colors from coolors ```r Europe_plot<-ggplot(data = Europe_shp)+ geom_sf(colour = "#edf2f4", fill = "#2b2d42", size=0.5) Europe_plot ``` <!-- --> Change background color ```r Europe_plot<-Europe_plot+ theme( panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_rect(fill = '#edf2f4'), legend.background = element_rect(fill = '#edf2f4')) Europe_plot ``` <!-- --> --- ## 3.4. Add limits Reduce to focus in your area of interest ```r Europe_plot<-Europe_plot+ coord_sf(xlim = c(6, 10),ylim = c(53, 56))+ scale_x_continuous(labels = function(x) paste0(x, '\u00B0', "W")) + scale_y_continuous(labels = function(x) paste0(x, '\u00B0', "N")) Europe_plot ``` <!-- --> --- name: scale ## 3.5. Add scale Load the package ggspatial to add a scale and an north arrow ```r library(ggspatial) ``` br is from bottom right bar_cols is for column colors text_col is for the color of the text ```r Europe_plot<-Europe_plot+ annotation_scale(location = "br",bar_cols = c("#ef233c", "#d90429"),text_col = '#ef233c') Europe_plot ``` <!-- --> --- name: arrow ## 3.6. Add arrow tl is for top left which_north preferably true (see why here) north_arrow_fancy_orienteering (see other styles here) ```r Europe_plot<-Europe_plot+ annotation_north_arrow(location = "tl", style = north_arrow_minimal(text_col = '#ffd60a', line_col = '#ffd60a', fill = '#ffd60a')) Europe_plot ``` <!-- --> --- ## 3.7. Add points The package **seamonas** contains data from a hypothetical survey. ```r library(seamonas) ``` Add the data to your environment. <br> The data frame contains the columns **latitude** and **longitude**. ```r survey_4326<-survey_4326 ``` Plot the data from the survey. ```r ggplot()+ geom_point(data=survey_4326, aes(x = longitude, y= latitude)) ``` <!-- --> --- ## 3.7. Add points Now add the points into the your map. ```r Europe_plot+ geom_point(data=survey_4326, aes(x = longitude, y= latitude), color='#2a9d8f') ``` <!-- --> --- ## 3.7. Add points You could even change the size of the points. ```r Europe_plot+ geom_point(data=survey_4326, aes(x = longitude, y= latitude), color='#2a9d8f', * size=0.1) ``` <!-- -->